Knowledge as a contingency variable: Do the characteristics of knowledge predict organization structure?

• 成果类型: Article
• 署名作者: Birkinshaw, J; Nobel, R; Ridderstråle, J
• 署名单位: University of London; London Business School; Stockholm School of Economics
• 刊物名称: ORGANIZATION SCIENCE
• ISSN/ISSBN: 1047-7039
• DOI: 10.1287/orsc.13.3.274.2778
• 发表日期: 2002
• 页码: 274-289
• 关键词: KNOWLEDGE MANAGEMENT; CONTINGENCY THEORY; R&D management

摘要

This paper examines the validity of knowledge as a contingency variable. Building on recent advances in thinking about the dimensions of knowledge assets (Winter 1987. Zander and Kogut 1995). we argue that such dimensions might have an Important influence on organization structure. More specifically, we focus on two dimensions of knowledge-observability and system embeddedness-and their influence over the level of unit autonomy and interunit integration in an international network of R&D units. Statistical analysis of questionnaire responses from 110 R&D unit managers show strong association between the dimensions of knowledge and organization structure. It also indicates partial support for the fit hypothesis in contingency theory. The paper makes two important contributions to the knowledge management literature. First. we find support for the contingency logic. suggesting that effective organization design has to take into account the underlying characteristics of the firm's knowledge base. Second, we shed light on a relatively neglected dimension of knowledge that we call system embeddedness. This is the extent to which knowledge is a function of the social and physical system in which it exists. In the statistical analysis it emerges as a strong predictor of organization structure. Moreover, it also appears to be conceptually distinct from the tacit-articulate dimension that is normally emphasized, This allows us to speculate on four generic forms that a firm's knowledge might take. that we label integrated. isolated. opaque. and transparent. These are discussed using example,, from the data.